1. Field of the Invention
The present invention relates to a channel estimator, and more particularly, to a channel estimator adopting masking.
2. Discussion of the Related Art
Generally, a terrestrial TV signal of an 8-VSB (vestigial sideband) system passes through a dynamic multipath channel from a transmitting end to arrive at a receiving end. The received signal is severely distorted by interference with an adjacent signal, whereby adoption of an equalizer is needed to restore an original signal from the distorted signal.
A receiver, which receives a signal transmitted from a single carrier transmission system adopting VSB, mostly uses an equalizer implemented in a time domain (hereinafter called time domain equalizer). And, a non-linear decision feedback equalizer (DEF) is the most representative.
The time domain equalizer adopts LMS (least-mean-squares) to facilitate its implementation. Yet, since error propagation occurs in case of severe channel distortion, the time domain equalizer fails to achieve re-convergence even if a channel status becomes good again. Moreover, the time domain equalizer fails to operate robustly when a main path in a time variable channel varies, frame synchronization of the received signal is frequently broken.
Most of the above-described defects of the time domain equalizer can be settled by means of using an equalizer that adopts zero forcing in a frequency domain after finding an impulse response of a channel using a channel estimator.
Namely, the equalizer implemented within the frequency domain enables to show its performance constantly regardless of the number of multipath or a degree of distortion of the received signal. And, the equalizer enables to operate robustly without generating frame errors like the time domain equalizer under a channel environment that a main path varies according to time. Moreover, the equalizer can be implemented more simply than the time domain equalizer as a range of multipath that is to be equalized gets wider.
In this case, performance of the frequency domain equalizer through channel estimation substantially depends on accuracy and frequency of the channel estimation, whereby various estimating methods are actively tried.
A system, to which a frequency domain equalizer through channel estimation is applied, according to a related art is explained by referring to the attached drawing as follows.
FIG. 1 is a block diagram of a system to which a frequency domain equalizer through channel estimation is applied according to a related art.
Referring to FIG. 1, the system consists of a channel estimator 101 estimating a CIR (channel impulse response) ĥ(n) from a received baseband signal y(n), a first FFT (frequency field transform) 102 transforming the base band signal into a signal of a frequency domain, a second FFT 103 transforming the estimated CIR ĥ(n) into a signal of the frequency domain, a frequency domain equalizer 104 executing equalization by zero forcing using the baseband signal y(n) and CIR ĥ(n) transformed into the signals of the frequency domain, respectively, and an IFFT 105 inverse-transforming the equalized signals of the frequency domain into signals of a time domain.
A signal equalized by zero forcing includes a colored noise. In order to remove such a colored noise, the system further consists of a noise predictor 106 computing a noise prediction value {circumflex over (v)}(n) from an output ĥ(n)+v(n) of the IFFT 105 and an adder 108 removing the colored noise by adding the output ĥ(n)+v(n) of the IFFT 105 to the noise prediction value {circumflex over (v)}(n) outputted from the noise predictor 106.
Specifically, the channel estimator 101 is explained in detail as follows.
As estimating methods of the channel estimator 101, there are an estimating method using a training signal only, the blind method operating regardless of a training signal, and the semi-blind method using a training signal for a training section and operating as blind for the rest section except the training section.
Three kinds of methods, which can be simply implemented relatively, among practical implementations of the above-explained two methods are explained as follows.
1) A Method Using Cross Correlation between Noted Training Signal and Actually-Received Training Signal Only
In case of a channel estimator using cross correlation only, a channel estimating value is relatively inaccurate despite its simplicity. In this case, in order to enhance accuracy of the cross correlation, it may be considered to increase the number of training symbols used for cross correlating calculation. Yet, since a peak shows up due to finiteness of PN sequence occurring by 63-symbol cycle, the structure of a VSB field sink part, where PN63 sequence is overlapped, has difficulty in accurate channel estimation.
2) Least Square (LS)
The constraint of the above method 1) can be overcome by channel estimation of LS. Namely, the LS removes auto correlating relation between training symbols used in cross correlating calculation within a training section from cross correlation, thereby fitting VSB reception environment.
3) Sub-Channel Response Matching (SRM) Using Both Data and Training Symbol
In case of sub-channel response matching (SRM), it is able to estimate a channel accurately only if a length of CIR is accurately known. Hence, SRM is not appropriate for an environment that a characteristic of such a certain channel as a terrestrial channel varies according to time severely.
Hence, in case of a channel estimator using cross correlation including LS, when noise caused by data is included while the cross correlation is executed, the influence of the noise caused by the data can be reduced by taking a time domain average using the fact that the data is a probability function of which average is ‘0’. Thus, in case of a channel estimator using time average, the accuracy of the channel estimating value can be enhanced by finding a channel estimation sequence value every field and by taking the average of the time domain.
However, since a moving average should be taken to remove a bad influence of data included in CIR, very poor characteristics appear on the time domain estimator updated by symbol unit in case of a time variable channel.
Meanwhile, as another method of removing the influence of data in channel estimation, there is a technique of thresholding, in which CIR, as shown in Equation 1, below a specific level (ε) is regarded as ‘0’.ĥ(n)=0, if |ĥ(n)|<εĥ(n), otherwise.  [Equation 1]
The channel estimator, which takes the time average on the CIR of the channel estimated by the LS method, accurately estimates the CIR of the channel for a static channel that is a multipath, thereby providing characteristics remarkably superior to those of the equalizer operating as blind in the time domain.
However, in case of the above-explained method, a CIR value exceeding a critical value fails to save pulse tails by a pulse sharing filter of a transmitting end and a matched filter of a receiving end at all, whereby degradation of performance severely occurs.
Besides the above-explained problems, there exists a certain amount of disturbance of a frequency spectrum despite an excellent terrestrial DTV reception environment, whereby reception performance on a dynamic multipath channel is degraded when the related art channel estimator is applied to a DTV receiver.